About Us | Help Videos | Contact Us | Subscriptions
 
 

The Plant Genome : Just Published

 

Accepted, edited articles are published here after author proofing to provide rapid publication and better access to the newest research. Articles are compiled into issues at dl.sciencesocieties.org/publications/tpg, which includes the complete archive.

Citation | Articles posted here are considered published and may be cited by the doi.

Joseph, B., J.A. Schlueter, J.Du, M.A. Graham, J. Ma, and R.C. Shoemaker. 2009. Retrotransposons within Syntenic Regions between Soybean and Medicago truncatula and Their Contribution to Local Genome Evolution. Plant Genome doi:10.3835/plantgenome2009.01.0001

Current issue: Plant Genome 10(1)



  • ORGINAL RESEARCH

    • Surbhi Grewal, Laura-Jayne Gardiner, Barbora Ndreca, Emilie Knight, Graham Moore, Ian P. King and Julie King
      Comparative Mapping and Targeted-Capture Sequencing of the Gametocidal Loci in Aegilops sharonensis

      Gametocidal (Gc) chromosomes or elements in species such as Aegilops sharonensis Eig are preferentially transmitted to the next generation through both the male and female gametes when introduced into wheat (Triticum aestivum L.). Furthermore, any genes, such as genes that control agronomically important traits, showing complete linkage with Gc elements, are also transmitted preferentially to the next generation without the need for selection. The mechanism for the preferential transmission of the Gc elements appears to occur by the induction of extensive chromosome damage in any gametes that lack the Gc chromosome in question. Previous studies on the mechanism of the Gc action in Ae. (continued)


      doi:10.3835/plantgenome2016.09.0090
      Published: May 11, 2017



  • ORIGINAL RESEARCH

    • Diego Jarquín, Cristiano Lemes da Silva, R. Chris Gaynor, Jesse Poland, Allan Fritz, Reka Howard, Sarah Battenfield and Jose Crossa
      Increasing Genomic-Enabled Prediction Accuracy by Modeling Genotype × Environment Interactions in Kansas Wheat

      Wheat (Triticum aestivum L.) breeding programs test experimental lines in multiple locations over multiple years to get an accurate assessment of grain yield and yield stability. Selections in early generations of the breeding pipeline are based on information from only one or few locations and thus materials are advanced with little knowledge of the genotype × environment interaction (G × E) effects. Later, large trials are conducted in several locations to assess the performance of more advanced lines across environments. Genomic selection (GS) models that include G × E covariates allow us to borrow information not only from related materials, but also from historical and correlated environments to better predict performance within and across specific environments. (continued)

      Core Ideas:
      • Incorporating environmental covariates increases genomic selection accuracy.
      • G × E models can impute known lines into known environments with good accuracy.
      • Breeding programs may exploit genomic selection cross-validation schemes in trial designs.

      doi:10.3835/plantgenome2016.12.0130
      Published: June 8, 2017



    • Shiaoman Chao, Matthew N. Rouse, Maricelis Acevedo, Agnes Szabo-Hever, Harold Bockelman, J. Michael Bonman, Elias Elias, Daryl Klindworth and Steven Xu
      Evaluation of Genetic Diversity and Host Resistance to Stem Rust in USDA NSGC Durum Wheat Accessions

      The USDA–ARS National Small Grains Collection (NSGC) maintains germplasm representing global diversity of small grains and their wild relatives. To evaluate the utility of the NSGC durum wheat (Triticum turgidum L. ssp. durum) accessions, we assessed genetic diversity and linkage disequilibrium (LD) patterns in a durum core subset containing 429 lines with spring growth habit originating from 64 countries worldwide. (continued)

      Core Ideas:
      • Characterized the utility of a core subset of USDA–NSGC worldwide durum wheat accessions
      • The durum core subset captured a considerable amount of genetic diversity
      • Identified accessions’ resistance to wheat stem rust pathogen races
      • Assessed genome-wide LD present in the durum core subset

      doi:10.3835/plantgenome2016.07.0071
      Published: June 8, 2017



    • Shiliang Cao, Alexander Loladze, Yibing Yuan, Yongsheng Wu, Ao Zhang, Jiafa Chen, Gordon Huestis, Jingsheng Cao, Vijay Chaikam, Michael Olsen, Boddupalli M. Prasanna, Felix San Vicente and Xuecai Zhang
      Genome-Wide Analysis of Tar Spot Complex Resistance in Maize Using Genotyping-by-Sequencing SNPs and Whole-Genome Prediction

      Tar spot complex (TSC) is one of the most destructive foliar diseases of maize (Zea mays L.) in tropical and subtropical areas of Central and South America, causing significant grain yield losses when weather conditions are conducive. To dissect the genetic architecture of TSC resistance in maize, association mapping, in conjunction with linkage mapping, was conducted on an association-mapping panel and three biparental doubled-haploid (DH) populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Association mapping revealed four quantitative trait loci (QTL) on chromosome 2, 3, 7, and 8. All the QTL, except for the one on chromosome 3, were further validated by linkage mapping in different genetic backgrounds. (continued)

      Core Ideas:
      • Association and linkage mapping are effective for dissecting genetic architecture of complex traits in maize.
      • TSC resistance in maize is controlled by a major QTL and several minor QTL.
      • Major QTL on bin 8.03 confirmed by association and linkage mapping.
      • TSC resistance in tropical maize could be improved by MAS and GS individually or stepwise.

      doi:10.3835/plantgenome2016.10.0099
      Published: May 25, 2017



    • Rex Bernardo
      Prospective Targeted Recombination and Genetic Gains for Quantitative Traits in Maize

      Advances in clustered regularly interspaced short palindromic repeats (CRISPR) technology have allowed targeted recombination in specific DNA sequences in yeast (Saccharomyces cerevisiae). My objective was to determine if the selection gains from targeted recombination are large enough to warrant the development of targeted recombination technology in plants. Genomewide marker effects for quantitative traits in two maize (Zea mays L.) experiments were used to identify targeted recombination points that would maximize the per-chromosome genetic gains in a given cross. With nontargeted recombination in the intermated B73 × Mo17 population, selecting the best out of 180 recombinant inbreds led to a 7.1% gain for testcross yield. (continued)


      doi:10.3835/plantgenome2016.11.0118
      Published: May 18, 2017



    • Jin Sun, Jessica E. Rutkoski, Jesse A. Poland, José Crossa, Jean-Luc Jannink and Mark E. Sorrells
      Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield

      High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat (Triticum aestivum L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. (continued)

      Core Ideas:
      • HTP platforms used to measure secondary traits across time
      • Longitudinal data of secondary traits evaluated by SR, MT, and RR models, separately
      • BLUPs of secondary traits used in the multivariate pedigree and genomic prediction
      • Grain yield predictive ability was improved by 70%

      doi:10.3835/plantgenome2016.11.0111
      Published: May 18, 2017



    • Stefano Pavan, Concetta Lotti, Angelo R. Marcotrigiano, Rosa Mazzeo, Nicoletta Bardaro, Valentina Bracuto, Francesca Ricciardi, Francesca Taranto, Nunzio D’Agostino, Adalgisa Schiavulli, Claudio De Giovanni, Cinzia Montemurro, Gabriella Sonnante and Luigi Ricciardi
      A Distinct Genetic Cluster in Cultivated Chickpea as Revealed by Genome-wide Marker Discovery and Genotyping

      The accurate description of plant biodiversity is of utmost importance to efficiently address efforts in conservation genetics and breeding. Herein, we report the successful application of a genotyping-by-sequencing (GBS) approach in chickpea (Cicer arietinum L.), resulting in the characterization of a cultivated germplasm collection with 3187 high-quality single nucleotide polymorphism (SNP) markers. Genetic structure inference, principal component analysis, and hierarchical clustering all indicated the identification of a genetic cluster corresponding to black-seeded genotypes traditionally cultivated in Southern Italy. Remarkably, this cluster was clearly distinct at both genetic and phenotypic levels from germplasm groups reflecting commercial chickpea classification into desi and kabuli seed types. (continued)

      Core Ideas:
      • Genotyping-by-sequencing analysis in cultivated chickpea generated 3187 high-quality single nucleotide polymorphisms.
      • Analysis of genetic diversity supports the identification of three subpopulations.
      • Accessions traditionally grown in Italy form a clearly distinct genetic cluster.
      • We identified genomic regions putatively resulting from directional selection.
      • Our findings are of interest for chickpea conservation genetics and breeding.

      doi:10.3835/plantgenome2016.11.0115
      Published: May 18, 2017



    • Yaopeng Zhou, Benjamin Conway, Daniela Miller, David Marshall, Aaron Cooper, Paul Murphy, Shiaoman Chao, Gina Brown-Guedira and José Costa
      Quantitative Trait Loci Mapping for Spike Characteristics in Hexaploid Wheat

      Wheat (Triticum aestivum L.) spike characteristics determine the number of grains produced on each spike and constitute key components of grain yield. Understanding of the genetic basis of spike characteristics in wheat, however, is limited. In this study, genotyping-by-sequencing (GBS) and the iSelect 9K assay were used on a doubled-haploid (DH) soft red winter wheat population that showed a wide range of phenotypic variation for spike traits. A genetic map spanning 2934.1 cM with an average interval length of 3.4 cM was constructed. (continued)

      Core Ideas:
      • A high-density wheat genetic map was generated using GBS and the 9K iSelect Array.
      • Spike characteristics were mainly determined by additive effects.
      • Major QTL of spike characteristics were identified on chromosomes 1A, 2B, and 5A.

      doi:10.3835/plantgenome2016.10.0101
      Published: May 5, 2017



    • Paulino Pérez-Rodríguez, José Crossa, Jessica Rutkoski, Jesse Poland, Ravi Singh, Andrés Legarra, Enrique Autrique, Gustavo de los Campos, Juan Burgueño and Susanne Dreisigacker
      Single-Step Genomic and Pedigree Genotype × Environment Interaction Models for Predicting Wheat Lines in International Environments

      Genomic prediction models have been commonly used in plant breeding but only in reduced datasets comprising a few hundred genotyped individuals. However, pedigree information for an entire breeding population is frequently available, as are historical data on the performance of a large number of selection candidates. The single-step method extends the genomic relationship information from genotyped individuals to pedigree information from a larger number of phenotyped individuals in order to combine relationship information on all members of the breeding population. Furthermore, genomic prediction models that incorporate genotype × environment interactions (G × E) have produced substantial increases in prediction accuracy compared with single-environment genomic prediction models. (continued)

      Core Ideas:
      • Genomic prediction accuracy models have been commonly used in plant breeding but only in reduced datasets comprising a few hundred genotyped individual plants.
      • In this study we used pedigree and genomic data from 58,798 wheat lines evaluated in different environments.
      • We use pedigree and genomic information in a model that incorporates genotype × environment interactions to predict wheat line performance in environments in South Asia.

      doi:10.3835/plantgenome2016.09.0089
      Published: May 5, 2017



    • Kathy Esvelt Klos, Belayneh A. Yimer, Ebrahiem M. Babiker, Aaron D. Beattie, J. Michael Bonman, Martin L. Carson, James Chong, Stephen A. Harrison, Amir M.H. Ibrahim, Frederic L. Kolb, Curt A. McCartney, Michael McMullen, Jennifer Mitchell Fetch, Mohsen Mohammadi, J. Paul Murphy and Nicholas A. Tinker
      Genome-Wide Association Mapping of Crown Rust Resistance in Oat Elite Germplasm

      Oat crown rust, caused by Puccinia coronata f. sp. avenae, is a major constraint to oat (Avena sativa L.) production in many parts of the world. In this first comprehensive multienvironment genome-wide association map of oat crown rust, we used 2972 single-nucleotide polymorphisms (SNPs) genotyped on 631 oat lines for association mapping of quantitative trait loci (QTL). (continued)

      Core Ideas:
      • Multienvironment genome-wide association study of reaction to crown rust in elite oat
      • Oat response to inoculation with 10 well-characterized Puccinia coronata isolates evaluated
      • Adult plant response to crown rust assessed in 10 location–years
      • Patterns of association compared against genotypes of differential gene stocks
      • QTL placed in the context of current literature

      doi:10.3835/plantgenome2016.10.0107
      Published: April 27, 2017



    • Qijian Song, Long Yan, Charles Quigley, Brandon D. Jordan, Edward Fickus, Steve Schroeder, Bao-Hua Song, Yong-Qiang Charles An, David Hyten, Randall Nelson, Katy Rainey, William D Beavis, Jim Specht, Brian Diers and Perry Cregan
      Genetic Characterization of the Soybean Nested Association Mapping Population

      A set of nested association mapping (NAM) families was developed by crossing 40 diverse soybean [Glycine max (L.) Merr.] genotypes to the common cultivar. The 41 parents were deeply sequenced for SNP discovery. Based on the polymorphism of the single-nucleotide polymorphisms (SNPs) and other selection criteria, a set of SNPs was selected to be included in the SoyNAM6K BeadChip for genotyping the parents and 5600 RILs from the 40 families. Analysis of the SNP profiles of the RILs showed a low average recombination rate. (continued)

      Core Ideas:
      • 40 NAM families were developed and 5600 RILs in the families were characterized.
      • The linkage maps for each family and a composite linkage map were constructed.
      • More than a half million high-confidence SNPs were identified and annotated.
      • Segregation distortion in most families favored alleles from the female parent.
      • The REs in the soybean genome is low.

      doi:10.3835/plantgenome2016.10.0109
      Published: April 27, 2017



    • Philomin Juliana, Ravi P. Singh, Pawan K. Singh, Jose Crossa, Jessica E. Rutkoski, Jesse A. Poland, Gary C. Bergstrom and Mark E. Sorrells
      Comparison of Models and Whole-Genome Profiling Approaches for Genomic-Enabled Prediction of Septoria Tritici Blotch, Stagonospora Nodorum Blotch, and Tan Spot Resistance in Wheat

      The leaf spotting diseases in wheat that include Septoria tritici blotch (STB) caused by Zymoseptoria tritici, Stagonospora nodorum blotch (SNB) caused by Parastagonospora nodorum, and tan spot (TS) caused by Pyrenophora tritici-repentis pose challenges to breeding programs in selecting for resistance. A promising approach that could enable selection prior to phenotyping is genomic selection that uses genome-wide markers to estimate breeding values (BVs) for quantitative traits. To evaluate this approach for seedling and/or adult plant resistance (APR) to STB, SNB, and TS, we compared the predictive ability of least-squares (LS) approach with genomic-enabled prediction models including genomic best linear unbiased predictor (GBLUP), Bayesian ridge regression (BRR), Bayes A (BA), Bayes B (BB), Bayes Cπ (BC), Bayesian least absolute shrinkage and selection operator (BL), and reproducing kernel Hilbert spaces markers (RKHS-M), a pedigree-based model (RKHS-P) and RKHS markers and pedigree (RKHS-MP). We observed that LS gave the lowest prediction accuracies and RKHS-MP, the highest. (continued)


      doi:10.3835/plantgenome2016.08.0082
      Published: April 6, 2017



    • Junping Chen, Ratan Chopra, Chad Hayes, Geoffrey Morris, Sandeep Marla, John Burke, Zhanguo Xin and Gloria Burow
      Genome-Wide Association Study of Developing Leaves’ Heat Tolerance during Vegetative Growth Stages in a Sorghum Association Panel

      Heat stress reduces grain yield and quality worldwide. Enhancing heat tolerance of crops at all developmental stages is one of the essential strategies required for sustaining agricultural production especially as frequency of temperature extremes escalates in response to climate change. Although heat tolerance mechanisms have been studied extensively in model plant species, little is known about the genetic control underlying heat stress responses of crop plants at the vegetative stage under field conditions. To dissect the genetic basis of heat tolerance in sorghum [Sorghum bicolor (L.) Moench], we performed a genome-wide association study (GWAS) for traits responsive to heat stress at the vegetative stage in an association panel. (continued)

      Core Ideas:
      • Sorghum could serve as a vital resource of heat tolerance DNA markers.
      • Natural variation of leaf traits provides understanding of heat tolerance in sorghum.
      • GWAS reveals 14 SNPs with two heat stress responsive traits in sorghum leaves.

      doi:10.3835/plantgenome2016.09.0091
      Published: March 27, 2017



    • Jose R. Lopez, John E. Erickson, Patricio Munoz, Ana Saballos, Terry J. Felderhoff and Wilfred Vermerris
      QTLs Associated with Crown Root Angle, Stomatal Conductance, and Maturity in Sorghum

      Three factors that directly affect the water inputs in cropping systems are root architecture, length of the growing season, and stomatal conductance to water vapor (gs). Deeper-rooted cultivars will perform better under water-limited conditions because they can access water stored deeper in the soil profile. Reduced gs limits transpiration rate (E) and thus throughout the vegetative phase conserves water that may be used during grain filling in water-limited environments. Additionally, growing early-maturing varieties in regions that rely on soil-stored water is a key water management strategy. (continued)

      Core Ideas:
      • QTLs for crown root angle, stomatal conductance, and maturity were identified in two field studies through the construction of a high-density bin map.
      • The QTL for stomatal conductance was associated with reduced leaf transpiration but not reduced net assimilation rate.
      • Candidate genes are proposed based on the physical location of the QTLs and the function of known genes in those locations.

      doi:10.3835/plantgenome2016.04.0038
      Published: March 27, 2017



    • Paolo Annicchiarico, Nelson Nazzicari, Luciano Pecetti, Massimo Romani, Barbara Ferrari, Yanling Wei and E. Charles Brummer
      GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought

      Terminal drought is the main stress that limits pea (Pisum sativum L.) grain yield in Mediterranean-climate regions. This study provides an unprecedented assessment of the predictive ability of genomic selection (GS) for grain yield under severe terminal drought using genotyping-by-sequencing (GBS) data. Additional aims were to assess the GS predictive ability for different GBS data quality filters and GS models, comparing intrapopulation with interpopulation GS predictive ability and to perform genome-wide association (GWAS) studies. The yield and onset of flowering of 315 lines from three recombinant inbred line (RIL) populations issued by connected crosses between three elite cultivars were assessed under a field rainout shelter. (continued)

      Core Ideas:
      • GBS-based genomic predictions of pea grain yield and phenology are accurate and cost-efficient.
      • Genomic areas related to high yield and early flowering colocate under severe terminal drought.
      • Cross-population genomic predictions have quite variable predictive ability.

      doi:10.3835/plantgenome2016.07.0072
      Published: March 20, 2017



  • Facebook   Twitter